Slip is one of the leading causes of fall-related injuries among occupational and elderly population. Existing research supports proactive slip and fall prevention approaches, while active strategies remain underdeveloped. Development of active slip-induced fall prevention systems requires fast, effective slip detection. This paper aims to develop a novel, real-time slip detection and estimation algorithm during human walking. The slip estimation is built on a slip dynamic model for biped walkers with the integration of the human locomotion constraints. The slip detection uses a set of wearable inertial sensors attached on the lower limbs. A slip indicator is introduced to detect the slip shortly after the heel-strike event. We present an extended Kalman filter-based slip estimation to characterize the slipping distance. One attractive property of the algorithm is its fast, accurate slip onset detection and slipping distance estimation with low-cost, nonintrusive sensing features. Experiments are conducted to validate and demonstrate the performance of the proposed slip detection and estimation scheme. Note to Practitioners - Slip-induced falls often lead to injuries among elderly and professional workers. Active slip and fall prevention could potentially mitigate the negative societal and economic impacts due to slip-induced injuries. However, no assistive device is yet commercially available or reported for slip and fall prevention. This paper presents a first step toward this goal by development and evaluation of a fast slip detection scheme. The wearable slip detection system is small size and lightweight, consisting of five small inertial measurements units placed on the leg. The system can detect the initiation of foot slip several times faster compared to the human sensorimotor mechanisms. The simplicity of the system design, easy implementation, and the fast detection results demonstrate superior reliability and robustness compared to the other slip detection approaches. The proposed slip detection algorithm can find potential applications, such as humanoid robots and control, and healthcare, or home automation.
|Original language||English (US)|
|Number of pages||13|
|Journal||IEEE Transactions on Automation Science and Engineering|
|State||Published - Jul 2019|
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Electrical and Electronic Engineering